In [1]:
import pandas as pd
import numpy as np                     # For mathematical calculations
import seaborn as sns                  # For data visualization
import matplotlib.pyplot as plt 
import seaborn as sn                   # For plotting graphs
import io
%matplotlib inline
import warnings                        # To ignore any warnings
warnings.filterwarnings("ignore")
In [2]:
filepath2 = r"C:\Users\91623\OneDrive\Desktop\forage\coutomer.xlsx"
df1= pd.read_excel(filepath2)
print(df1)

       transaction_id  product_id  customer_id transaction_date  online_order  \
0                 140          11          462       2017-01-01           0.0   
1                 517          77          311       2017-01-01           1.0   
2                 561          65         2298       2017-01-01           0.0   
3                1293          67         1931       2017-01-01           1.0   
4                1403           0         2891       2017-01-01           1.0   
...               ...         ...          ...              ...           ...   
19957           17394          20          933       2017-12-30           0.0   
19958           18609          27         2372       2017-12-30           0.0   
19959           19346           4          543       2017-12-30           0.0   
19960           19907          41         1721       2017-12-30           0.0   
19961           19959           4         1787       2017-12-30           0.0   

      order_status           brand product_line product_class product_size  \
0         Approved  Giant Bicycles     Standard          high       medium   
1         Approved        WeareA2B     Standard        medium       medium   
2         Approved        WeareA2B     Standard        medium       medium   
3         Approved  Norco Bicycles         Road        medium       medium   
4         Approved      OHM Cycles     Standard        medium       medium   
...            ...             ...          ...           ...          ...   
19957     Approved   Trek Bicycles     Standard        medium        small   
19958     Approved   Trek Bicycles     Standard        medium       medium   
19959     Approved  Giant Bicycles     Standard          high       medium   
19960     Approved  Norco Bicycles     Standard           low       medium   
19961     Approved           Solex     Standard        medium       medium   

       ...  past_3_years_bike_related_purchases   age        DOB  \
0      ...                                   32  62.0 1960-01-01   
1      ...                                   86  64.0 1957-08-16   
2      ...                                   64  24.0 1997-10-28   
3      ...                                   85  48.0 1973-11-06   
4      ...                                    5  35.0 1987-02-22   
...    ...                                  ...   ...        ...   
19957  ...                                   75  26.0 1996-05-19   
19958  ...                                   14  44.0 1977-09-10   
19959  ...                                    9  48.0 1973-08-03   
19960  ...                                   82  41.0 1980-10-22   
19961  ...                                    2  45.0 1976-11-21   

                     job_title job_industry_category     wealth_segment  \
0                          NaN         Manufacturing  Affluent Customer   
1             Internal Auditor                Health     High Net Worth   
2           Environmental Tech              Property      Mass Customer   
3             Senior Developer                Health  Affluent Customer   
4       Nuclear Power Engineer         Manufacturing     High Net Worth   
...                        ...                   ...                ...   
19957       Help Desk Operator                Retail      Mass Customer   
19958      Clinical Specialist                Health  Affluent Customer   
19959      Account Coordinator                Retail  Affluent Customer   
19960  Senior Quality Engineer    Financial Services     High Net Worth   
19961      Executive Secretary                Retail     High Net Worth   

      owns_car  tenure  state MTW Status  
0          Yes     5.0    NSW   Matching  
1           No    12.0    NSW   Matching  
2          Yes     5.0    VIC   Matching  
3           No     6.0    QLD   Matching  
4          Yes    12.0    NSW   Matching  
...        ...     ...    ...        ...  
19957      Yes     5.0    NSW   Matching  
19958       No    10.0    VIC   Matching  
19959      Yes    17.0    NSW   Matching  
19960       No    14.0    QLD   Matching  
19961      Yes    13.0    VIC   Matching  

[19962 rows x 27 columns]
In [3]:
df = df1
In [4]:
df.head()
Out[4]:
transaction_id product_id customer_id transaction_date online_order order_status brand product_line product_class product_size ... past_3_years_bike_related_purchases age DOB job_title job_industry_category wealth_segment owns_car tenure state MTW Status
0 140 11 462 2017-01-01 0.0 Approved Giant Bicycles Standard high medium ... 32 62.0 1960-01-01 NaN Manufacturing Affluent Customer Yes 5.0 NSW Matching
1 517 77 311 2017-01-01 1.0 Approved WeareA2B Standard medium medium ... 86 64.0 1957-08-16 Internal Auditor Health High Net Worth No 12.0 NSW Matching
2 561 65 2298 2017-01-01 0.0 Approved WeareA2B Standard medium medium ... 64 24.0 1997-10-28 Environmental Tech Property Mass Customer Yes 5.0 VIC Matching
3 1293 67 1931 2017-01-01 1.0 Approved Norco Bicycles Road medium medium ... 85 48.0 1973-11-06 Senior Developer Health Affluent Customer No 6.0 QLD Matching
4 1403 0 2891 2017-01-01 1.0 Approved OHM Cycles Standard medium medium ... 5 35.0 1987-02-22 Nuclear Power Engineer Manufacturing High Net Worth Yes 12.0 NSW Matching

5 rows × 27 columns

In [5]:
df=df.drop('transaction_id',axis=1)
In [6]:
df=df.drop('product_id',axis=1)
In [7]:
df=df.drop('customer_id',axis=1)
In [8]:
df=df.drop('MTW Status',axis=1)
In [9]:
df.head()
Out[9]:
transaction_date online_order order_status brand product_line product_class product_size list_price standard_cost product_first_sold_date ... gender past_3_years_bike_related_purchases age DOB job_title job_industry_category wealth_segment owns_car tenure state
0 2017-01-01 0.0 Approved Giant Bicycles Standard high medium 1274.93 764.96 42560.0 ... Female 32 62.0 1960-01-01 NaN Manufacturing Affluent Customer Yes 5.0 NSW
1 2017-01-01 1.0 Approved WeareA2B Standard medium medium 1769.64 108.76 40672.0 ... Female 86 64.0 1957-08-16 Internal Auditor Health High Net Worth No 12.0 NSW
2 2017-01-01 0.0 Approved WeareA2B Standard medium medium 1807.45 778.69 40410.0 ... Male 64 24.0 1997-10-28 Environmental Tech Property Mass Customer Yes 5.0 VIC
3 2017-01-01 1.0 Approved Norco Bicycles Road medium medium 544.05 376.84 38647.0 ... Male 85 48.0 1973-11-06 Senior Developer Health Affluent Customer No 6.0 QLD
4 2017-01-01 1.0 Approved OHM Cycles Standard medium medium 183.86 137.90 34170.0 ... Female 5 35.0 1987-02-22 Nuclear Power Engineer Manufacturing High Net Worth Yes 12.0 NSW

5 rows × 23 columns

In [10]:
df.shape
Out[10]:
(19962, 23)
In [11]:
unique = [feature for feature in df.columns if len(df[feature].unique())>0 and len(df[feature].unique())<100]
for feature in unique:
    print("{} has {} unique values : {} {}".format(feature,len(df[feature].unique()),df[feature].unique(),"\n"))

online_order has 3 unique values : [ 0.  1. nan] 

order_status has 2 unique values : ['Approved' 'Cancelled'] 

brand has 7 unique values : ['Giant Bicycles' 'WeareA2B' 'Norco Bicycles' 'OHM Cycles' 'Solex'
 'Trek Bicycles' nan] 

product_line has 5 unique values : ['Standard' 'Road' 'Touring' 'Mountain' nan] 

product_class has 4 unique values : ['high' 'medium' 'low' nan] 

product_size has 4 unique values : ['medium' 'large' 'small' nan] 

gender has 6 unique values : ['Female' 'Male' 'U' 'F' 'M' 'Femal'] 

age has 55 unique values : [ 62.  64.  24.  48.  35.  66.  54.  52.  47.  41.  60.  37.  68.  42.
  55.  30.  49.  33.  67.  65.  26.  22.  25.  29.  59.  44.  28.  45.
  32.  50.  57.  46.  34.  36.  53. 122.  40.  27.  51.  23.  63.  56.
  43.  90.  20.  61.  21.  39.  38.  58.  31.  78.  86.  81.  nan] 

job_industry_category has 10 unique values : ['Manufacturing' 'Health' 'Property' 'IT' 'Financial Services' 'Retail'
 nan 'Entertainment' 'Telecommunications' 'Argiculture'] 

wealth_segment has 3 unique values : ['Affluent Customer' 'High Net Worth' 'Mass Customer'] 

owns_car has 2 unique values : ['Yes' 'No'] 

tenure has 23 unique values : [ 5. 12.  6. 13. 16. 14. 18. 17. 19. 11.  9.  7. 15. 20.  8.  4.  1.  2.
 10. nan  3. 21. 22.] 

state has 5 unique values : ['NSW' 'VIC' 'QLD' 'New South Wales' 'Victoria']
In [12]:
df.dropna()
Out[12]:
transaction_date online_order order_status brand product_line product_class product_size list_price standard_cost product_first_sold_date ... gender past_3_years_bike_related_purchases age DOB job_title job_industry_category wealth_segment owns_car tenure state
1 2017-01-01 1.0 Approved WeareA2B Standard medium medium 1769.64 108.76 40672.0 ... Female 86 64.0 1957-08-16 Internal Auditor Health High Net Worth No 12.0 NSW
2 2017-01-01 0.0 Approved WeareA2B Standard medium medium 1807.45 778.69 40410.0 ... Male 64 24.0 1997-10-28 Environmental Tech Property Mass Customer Yes 5.0 VIC
3 2017-01-01 1.0 Approved Norco Bicycles Road medium medium 544.05 376.84 38647.0 ... Male 85 48.0 1973-11-06 Senior Developer Health Affluent Customer No 6.0 QLD
4 2017-01-01 1.0 Approved OHM Cycles Standard medium medium 183.86 137.90 34170.0 ... Female 5 35.0 1987-02-22 Nuclear Power Engineer Manufacturing High Net Worth Yes 12.0 NSW
5 2017-01-01 1.0 Approved Solex Standard medium medium 1577.53 826.51 37220.0 ... Male 69 66.0 1956-03-17 Marketing Manager IT Mass Customer No 5.0 NSW
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
19957 2017-12-30 0.0 Approved Trek Bicycles Standard medium small 1775.81 1580.47 35160.0 ... Female 75 26.0 1996-05-19 Help Desk Operator Retail Mass Customer Yes 5.0 NSW
19958 2017-12-30 0.0 Approved Trek Bicycles Standard medium medium 499.53 388.72 36334.0 ... Male 14 44.0 1977-09-10 Clinical Specialist Health Affluent Customer No 10.0 VIC
19959 2017-12-30 0.0 Approved Giant Bicycles Standard high medium 1129.13 677.48 40649.0 ... Male 9 48.0 1973-08-03 Account Coordinator Retail Affluent Customer Yes 17.0 NSW
19960 2017-12-30 0.0 Approved Norco Bicycles Standard low medium 958.74 748.90 38693.0 ... Female 82 41.0 1980-10-22 Senior Quality Engineer Financial Services High Net Worth No 14.0 QLD
19961 2017-12-30 0.0 Approved Solex Standard medium medium 1483.20 99.59 38647.0 ... Male 2 45.0 1976-11-21 Executive Secretary Retail High Net Worth Yes 13.0 VIC

13622 rows × 23 columns

In [13]:
df.isnull().sum()
Out[13]:
transaction_date                          0
online_order                            359
order_status                              0
brand                                   195
product_line                            195
product_class                           195
product_size                            195
list_price                                0
standard_cost                           195
product_first_sold_date                 195
product_first_sold_date.1               195
first_name                                0
last_name                               642
gender                                    0
past_3_years_bike_related_purchases       0
age                                       9
DOB                                     446
job_title                              2379
job_industry_category                  3222
wealth_segment                            0
owns_car                                  0
tenure                                  446
state                                     0
dtype: int64
In [14]:
df=df.dropna()
In [15]:
df.isnull().sum()
Out[15]:
transaction_date                       0
online_order                           0
order_status                           0
brand                                  0
product_line                           0
product_class                          0
product_size                           0
list_price                             0
standard_cost                          0
product_first_sold_date                0
product_first_sold_date.1              0
first_name                             0
last_name                              0
gender                                 0
past_3_years_bike_related_purchases    0
age                                    0
DOB                                    0
job_title                              0
job_industry_category                  0
wealth_segment                         0
owns_car                               0
tenure                                 0
state                                  0
dtype: int64
In [16]:
unique = [feature for feature in df.columns if len(df[feature].unique())>0 and len(df[feature].unique())<100]
for feature in unique:
    print("{} has {} unique values : {} {}".format(feature,len(df[feature].unique()),df[feature].unique(),"\n"))

online_order has 2 unique values : [1. 0.] 

order_status has 2 unique values : ['Approved' 'Cancelled'] 

brand has 6 unique values : ['WeareA2B' 'Norco Bicycles' 'OHM Cycles' 'Solex' 'Giant Bicycles'
 'Trek Bicycles'] 

product_line has 4 unique values : ['Standard' 'Road' 'Touring' 'Mountain'] 

product_class has 3 unique values : ['medium' 'high' 'low'] 

product_size has 3 unique values : ['medium' 'large' 'small'] 

gender has 4 unique values : ['Female' 'Male' 'F' 'Femal'] 

age has 52 unique values : [64. 24. 48. 35. 66. 54. 52. 47. 41. 62. 60. 37. 68. 55. 33. 67. 26. 22.
 25. 59. 44. 28. 45. 32. 50. 49. 46. 34. 53. 42. 40. 27. 36. 51. 23. 65.
 57. 43. 63. 90. 61. 29. 21. 39. 30. 56. 38. 58. 20. 31. 78. 86.] 

job_industry_category has 9 unique values : ['Health' 'Property' 'Manufacturing' 'IT' 'Financial Services' 'Retail'
 'Entertainment' 'Telecommunications' 'Argiculture'] 

wealth_segment has 3 unique values : ['High Net Worth' 'Mass Customer' 'Affluent Customer'] 

owns_car has 2 unique values : ['No' 'Yes'] 

tenure has 22 unique values : [12.  5.  6. 13. 16. 14. 18. 19. 11. 17. 15.  7. 20.  4.  1.  9.  8. 10.
  2.  3. 21. 22.] 

state has 5 unique values : ['NSW' 'VIC' 'QLD' 'New South Wales' 'Victoria']
In [17]:
df['gender'].replace(to_replace=['F','Femal','U'] , value='Female', inplace=True)
In [18]:
df['gender'].replace(to_replace='M' , value='Male', inplace=True)
In [19]:
df['state'].replace(to_replace='New South Wales' , value='NSW', inplace=True)
In [20]:
df['state'].replace(to_replace='Victoria' , value='VIC', inplace=True)
In [21]:
df.columns = df.columns.str.lower()
In [22]:
sns.countplot(df['gender'],palette='dark:r');
In [23]:
sns.countplot(df['state'],palette='dark:r');
In [24]:
sns.countplot(df['brand'],palette='dark:r');
In [25]:
sns.countplot(df['order_status'],palette='dark:r');
In [26]:
sns.countplot(df['wealth_segment'],palette='dark:r');
In [27]:
sns.countplot(df['online_order'],palette='dark:r');
In [28]:
from datetime import datetime
from datetime import date
In [29]:
df.head()
Out[29]:
transaction_date online_order order_status brand product_line product_class product_size list_price standard_cost product_first_sold_date ... gender past_3_years_bike_related_purchases age dob job_title job_industry_category wealth_segment owns_car tenure state
1 2017-01-01 1.0 Approved WeareA2B Standard medium medium 1769.64 108.76 40672.0 ... Female 86 64.0 1957-08-16 Internal Auditor Health High Net Worth No 12.0 NSW
2 2017-01-01 0.0 Approved WeareA2B Standard medium medium 1807.45 778.69 40410.0 ... Male 64 24.0 1997-10-28 Environmental Tech Property Mass Customer Yes 5.0 VIC
3 2017-01-01 1.0 Approved Norco Bicycles Road medium medium 544.05 376.84 38647.0 ... Male 85 48.0 1973-11-06 Senior Developer Health Affluent Customer No 6.0 QLD
4 2017-01-01 1.0 Approved OHM Cycles Standard medium medium 183.86 137.90 34170.0 ... Female 5 35.0 1987-02-22 Nuclear Power Engineer Manufacturing High Net Worth Yes 12.0 NSW
5 2017-01-01 1.0 Approved Solex Standard medium medium 1577.53 826.51 37220.0 ... Male 69 66.0 1956-03-17 Marketing Manager IT Mass Customer No 5.0 NSW

5 rows × 23 columns

In [30]:
df.columns
Out[30]:
Index(['transaction_date', 'online_order', 'order_status', 'brand',
       'product_line', 'product_class', 'product_size', 'list_price',
       'standard_cost', 'product_first_sold_date', 'product_first_sold_date.1',
       'first_name', 'last_name', 'gender',
       'past_3_years_bike_related_purchases', 'age', 'dob', 'job_title',
       'job_industry_category', 'wealth_segment', 'owns_car', 'tenure',
       'state'],
      dtype='object')
In [31]:
sns.histplot(data =df['age'])
Out[31]:
<AxesSubplot:xlabel='age', ylabel='Count'>
In [32]:
df = df.drop(df[df['age']>70].index)
In [33]:
sns.histplot(data =df['age'])
Out[33]:
<AxesSubplot:xlabel='age', ylabel='Count'>
In [34]:
 df.to_excel('clean_data_forger.xls', index=False)
In [35]:
numerical_features = [feature for feature in df.columns if df[feature].dtype != "O"]
print("we have {} numerical features out of {}".format(len(numerical_features),len(df.columns)))

we have 9 numerical features out of 23
In [36]:
discrete_features = [feature for feature in numerical_features if len(df[feature].unique())<25]
print("we have {} discrete features out of {} total features".format(len(discrete_features),len(df.columns)))

we have 2 discrete features out of 23 total features
In [37]:
df[discrete_features].head()
Out[37]:
online_order tenure
1 1.0 12.0
2 0.0 5.0
3 1.0 6.0
4 1.0 12.0
5 1.0 5.0
In [38]:
fig,ax = plt.subplots(4,4,figsize=(20,15))
for variable, subplot in zip(discrete_features,ax.flatten()):
    sns.countplot(df[variable],ax=subplot,palette="winter")
In [39]:
continuos_features = [feature for feature in numerical_features if feature not in discrete_features]
print("we have {} continuos features out of {} total features".format(len(continuos_features),len(df.columns)))

we have 7 continuos features out of 23 total features
In [40]:
fig,ax = plt.subplots(3,3,figsize=(20,12))
for variable, subplot in zip(continuos_features,ax.flatten()):
    sns.histplot(df[variable],ax=subplot,palette="winter")
In [41]:
categorical_features = [feature for feature in df.columns if df[feature].dtype == "O"]
print("we have {} continuos features out of {} features".format(len(categorical_features),len(df.columns)))

we have 14 continuos features out of 23 features
In [42]:
df[categorical_features].head()
Out[42]:
order_status brand product_line product_class product_size product_first_sold_date.1 first_name last_name gender job_title job_industry_category wealth_segment owns_car state
1 Approved WeareA2B Standard medium medium 2016-07-10 00:00:00 Jaimie Halms Female Internal Auditor Health High Net Worth No NSW
2 Approved WeareA2B Standard medium medium 2016-07-11 00:00:00 Brigham Vyse Male Environmental Tech Property Mass Customer Yes VIC
3 Approved Norco Bicycles Road medium medium 2016-07-12 00:00:00 Boris Syres Male Senior Developer Health Affluent Customer No QLD
4 Approved OHM Cycles Standard medium medium 2016-07-13 00:00:00 Renae Ughetti Female Nuclear Power Engineer Manufacturing High Net Worth Yes NSW
5 Approved Solex Standard medium medium 2016-07-14 00:00:00 Stewart Brosoli Male Marketing Manager IT Mass Customer No NSW
In [43]:
fig,ax = plt.subplots(4,4,figsize=(20,15))
for variable, subplot in zip(categorical_features,ax.flatten()):
    sns.countplot(df[variable],ax=subplot,palette="winter")
In [44]:
sns.set_palette(sns.color_palette("nipy_spectral", 8))

sns.swarmplot(y='job_industry_category',x='age',hue='brand',data=df)
sns.despine(top=True,right=True,left=True)
In [45]:
df=df.drop('job_title',axis=1)
In [46]:
df=df.drop('first_name',axis=1)
In [47]:
df=df.drop('last_name',axis=1)
In [48]:
df=df.drop('dob',axis=1)
In [49]:
df=df.drop('transaction_date',axis=1)
In [50]:
df=df.drop('product_first_sold_date.1', axis=1)
In [51]:
df.columns
Out[51]:
Index(['online_order', 'order_status', 'brand', 'product_line',
       'product_class', 'product_size', 'list_price', 'standard_cost',
       'product_first_sold_date', 'gender',
       'past_3_years_bike_related_purchases', 'age', 'job_industry_category',
       'wealth_segment', 'owns_car', 'tenure', 'state'],
      dtype='object')
In [52]:
corrmat=df.corr()
top_corr=corrmat.index
plt.figure(figsize=(20,20))
g= sns.heatmap(df[top_corr].corr(),annot= True,cmap="RdYlGn")
In [53]:
sns.pairplot(df,hue='brand')
Out[53]:
<seaborn.axisgrid.PairGrid at 0x1901be7bc70>
In [54]:
df.head()
Out[54]:
online_order order_status brand product_line product_class product_size list_price standard_cost product_first_sold_date gender past_3_years_bike_related_purchases age job_industry_category wealth_segment owns_car tenure state
1 1.0 Approved WeareA2B Standard medium medium 1769.64 108.76 40672.0 Female 86 64.0 Health High Net Worth No 12.0 NSW
2 0.0 Approved WeareA2B Standard medium medium 1807.45 778.69 40410.0 Male 64 24.0 Property Mass Customer Yes 5.0 VIC
3 1.0 Approved Norco Bicycles Road medium medium 544.05 376.84 38647.0 Male 85 48.0 Health Affluent Customer No 6.0 QLD
4 1.0 Approved OHM Cycles Standard medium medium 183.86 137.90 34170.0 Female 5 35.0 Manufacturing High Net Worth Yes 12.0 NSW
5 1.0 Approved Solex Standard medium medium 1577.53 826.51 37220.0 Male 69 66.0 IT Mass Customer No 5.0 NSW
In [55]:
df3=df.copy()
In [56]:
df.head()
Out[56]:
online_order order_status brand product_line product_class product_size list_price standard_cost product_first_sold_date gender past_3_years_bike_related_purchases age job_industry_category wealth_segment owns_car tenure state
1 1.0 Approved WeareA2B Standard medium medium 1769.64 108.76 40672.0 Female 86 64.0 Health High Net Worth No 12.0 NSW
2 0.0 Approved WeareA2B Standard medium medium 1807.45 778.69 40410.0 Male 64 24.0 Property Mass Customer Yes 5.0 VIC
3 1.0 Approved Norco Bicycles Road medium medium 544.05 376.84 38647.0 Male 85 48.0 Health Affluent Customer No 6.0 QLD
4 1.0 Approved OHM Cycles Standard medium medium 183.86 137.90 34170.0 Female 5 35.0 Manufacturing High Net Worth Yes 12.0 NSW
5 1.0 Approved Solex Standard medium medium 1577.53 826.51 37220.0 Male 69 66.0 IT Mass Customer No 5.0 NSW
In [57]:
object_feat = [feature for feature in df3.columns if df3[feature].dtype == "O"]
df3[object_feat].head()
Out[57]:
order_status brand product_line product_class product_size gender job_industry_category wealth_segment owns_car state
1 Approved WeareA2B Standard medium medium Female Health High Net Worth No NSW
2 Approved WeareA2B Standard medium medium Male Property Mass Customer Yes VIC
3 Approved Norco Bicycles Road medium medium Male Health Affluent Customer No QLD
4 Approved OHM Cycles Standard medium medium Female Manufacturing High Net Worth Yes NSW
5 Approved Solex Standard medium medium Male IT Mass Customer No NSW
In [58]:
df.head()
Out[58]:
online_order order_status brand product_line product_class product_size list_price standard_cost product_first_sold_date gender past_3_years_bike_related_purchases age job_industry_category wealth_segment owns_car tenure state
1 1.0 Approved WeareA2B Standard medium medium 1769.64 108.76 40672.0 Female 86 64.0 Health High Net Worth No 12.0 NSW
2 0.0 Approved WeareA2B Standard medium medium 1807.45 778.69 40410.0 Male 64 24.0 Property Mass Customer Yes 5.0 VIC
3 1.0 Approved Norco Bicycles Road medium medium 544.05 376.84 38647.0 Male 85 48.0 Health Affluent Customer No 6.0 QLD
4 1.0 Approved OHM Cycles Standard medium medium 183.86 137.90 34170.0 Female 5 35.0 Manufacturing High Net Worth Yes 12.0 NSW
5 1.0 Approved Solex Standard medium medium 1577.53 826.51 37220.0 Male 69 66.0 IT Mass Customer No 5.0 NSW
In [ ]:
In [59]:
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()

for i in object_feat:
    df3[i] = df3[[i]].apply(le.fit_transform)
In [60]:
df3.head()
Out[60]:
online_order order_status brand product_line product_class product_size list_price standard_cost product_first_sold_date gender past_3_years_bike_related_purchases age job_industry_category wealth_segment owns_car tenure state
1 1.0 0 5 2 2 1 1769.64 108.76 40672.0 0 86 64.0 3 1 0 12.0 0
2 0.0 0 5 2 2 1 1807.45 778.69 40410.0 1 64 24.0 6 2 1 5.0 2
3 1.0 0 1 1 2 1 544.05 376.84 38647.0 1 85 48.0 3 0 0 6.0 1
4 1.0 0 2 2 2 1 183.86 137.90 34170.0 0 5 35.0 5 1 1 12.0 0
5 1.0 0 3 2 2 1 1577.53 826.51 37220.0 1 69 66.0 4 2 0 5.0 0
In [61]:
df.head()
Out[61]:
online_order order_status brand product_line product_class product_size list_price standard_cost product_first_sold_date gender past_3_years_bike_related_purchases age job_industry_category wealth_segment owns_car tenure state
1 1.0 Approved WeareA2B Standard medium medium 1769.64 108.76 40672.0 Female 86 64.0 Health High Net Worth No 12.0 NSW
2 0.0 Approved WeareA2B Standard medium medium 1807.45 778.69 40410.0 Male 64 24.0 Property Mass Customer Yes 5.0 VIC
3 1.0 Approved Norco Bicycles Road medium medium 544.05 376.84 38647.0 Male 85 48.0 Health Affluent Customer No 6.0 QLD
4 1.0 Approved OHM Cycles Standard medium medium 183.86 137.90 34170.0 Female 5 35.0 Manufacturing High Net Worth Yes 12.0 NSW
5 1.0 Approved Solex Standard medium medium 1577.53 826.51 37220.0 Male 69 66.0 IT Mass Customer No 5.0 NSW
In [62]:
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler() 
df2 = scaler.fit_transform(df3)
In [63]:
df2 = pd.DataFrame(df2,columns = df3.columns)
In [64]:
df2
Out[64]:
online_order order_status brand product_line product_class product_size list_price standard_cost product_first_sold_date gender past_3_years_bike_related_purchases age job_industry_category wealth_segment owns_car tenure state
0 1.000221 -0.099018 1.468835 0.329845 0.614039 0.095633 1.128566 -1.099350 0.849928 -0.977667 1.276818 1.599646 -0.433672 -0.303225 -1.012658 0.221493 -0.860632
1 -0.999779 -0.099018 1.468835 0.329845 0.614039 0.095633 1.193264 0.540603 0.758976 1.022843 0.507755 -1.620836 1.086380 0.907093 0.987500 -1.012823 1.528402
2 1.000221 -0.099018 -0.909254 -1.463136 0.614039 0.095633 -0.968569 -0.443104 0.146956 1.022843 1.241860 0.311453 -0.433672 -1.513543 -1.012658 -0.836492 0.333885
3 1.000221 -0.099018 -0.314732 0.329845 0.614039 0.095633 -1.584898 -1.028017 -1.407220 -0.977667 -1.554731 -0.735203 0.579696 -0.303225 0.987500 0.221493 -0.860632
4 1.000221 -0.099018 0.279791 0.329845 0.614039 0.095633 0.799842 0.657663 -0.348422 1.022843 0.682542 1.760670 0.073012 0.907093 -1.012658 -1.012823 -0.860632
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
13590 -0.999779 -0.099018 0.874313 0.329845 0.614039 1.806328 1.139124 2.503317 -1.063545 -0.977667 0.892286 -1.459811 1.593064 0.907093 0.987500 -1.012823 -0.860632
13591 -0.999779 -0.099018 0.874313 0.329845 0.614039 0.095633 -1.044748 -0.414023 -0.655994 1.022843 -1.240114 -0.010595 -0.433672 -1.513543 -1.012658 -0.131168 1.528402
13592 -0.999779 -0.099018 -1.503776 0.329845 -2.061985 0.095633 0.032575 0.292846 0.841944 1.022843 -1.414901 0.311453 1.593064 -1.513543 0.987500 1.103148 -0.860632
13593 -0.999779 -0.099018 -0.909254 0.329845 -0.723973 0.095633 -0.258983 0.467678 0.162925 -0.977667 1.136988 -0.252131 -0.940357 -0.303225 -1.012658 0.574155 0.333885
13594 -0.999779 -0.099018 0.279791 0.329845 0.614039 0.095633 0.638432 -1.121798 0.146956 1.022843 -1.659603 0.069917 1.593064 -0.303225 0.987500 0.397824 1.528402

13595 rows × 17 columns

In [65]:
df.head()
Out[65]:
online_order order_status brand product_line product_class product_size list_price standard_cost product_first_sold_date gender past_3_years_bike_related_purchases age job_industry_category wealth_segment owns_car tenure state
1 1.0 Approved WeareA2B Standard medium medium 1769.64 108.76 40672.0 Female 86 64.0 Health High Net Worth No 12.0 NSW
2 0.0 Approved WeareA2B Standard medium medium 1807.45 778.69 40410.0 Male 64 24.0 Property Mass Customer Yes 5.0 VIC
3 1.0 Approved Norco Bicycles Road medium medium 544.05 376.84 38647.0 Male 85 48.0 Health Affluent Customer No 6.0 QLD
4 1.0 Approved OHM Cycles Standard medium medium 183.86 137.90 34170.0 Female 5 35.0 Manufacturing High Net Worth Yes 12.0 NSW
5 1.0 Approved Solex Standard medium medium 1577.53 826.51 37220.0 Male 69 66.0 IT Mass Customer No 5.0 NSW
In [66]:
from sklearn.cluster import KMeans
from yellowbrick.cluster import KElbowVisualizer
model = KMeans()
visualizer = KElbowVisualizer(model, k=(1,17))
visualizer.fit(df2)
visualizer.show();
In [67]:
kmeans = KMeans(n_clusters=5,init='k-means++',random_state=0)
pred_kmeans = kmeans.fit_predict(df2)
In [68]:
df.head()
Out[68]:
online_order order_status brand product_line product_class product_size list_price standard_cost product_first_sold_date gender past_3_years_bike_related_purchases age job_industry_category wealth_segment owns_car tenure state
1 1.0 Approved WeareA2B Standard medium medium 1769.64 108.76 40672.0 Female 86 64.0 Health High Net Worth No 12.0 NSW
2 0.0 Approved WeareA2B Standard medium medium 1807.45 778.69 40410.0 Male 64 24.0 Property Mass Customer Yes 5.0 VIC
3 1.0 Approved Norco Bicycles Road medium medium 544.05 376.84 38647.0 Male 85 48.0 Health Affluent Customer No 6.0 QLD
4 1.0 Approved OHM Cycles Standard medium medium 183.86 137.90 34170.0 Female 5 35.0 Manufacturing High Net Worth Yes 12.0 NSW
5 1.0 Approved Solex Standard medium medium 1577.53 826.51 37220.0 Male 69 66.0 IT Mass Customer No 5.0 NSW
In [69]:
df['pred_kmeans'] = pred_kmeans+1
df.head()
Out[69]:
online_order order_status brand product_line product_class product_size list_price standard_cost product_first_sold_date gender past_3_years_bike_related_purchases age job_industry_category wealth_segment owns_car tenure state pred_kmeans
1 1.0 Approved WeareA2B Standard medium medium 1769.64 108.76 40672.0 Female 86 64.0 Health High Net Worth No 12.0 NSW 1
2 0.0 Approved WeareA2B Standard medium medium 1807.45 778.69 40410.0 Male 64 24.0 Property Mass Customer Yes 5.0 VIC 2
3 1.0 Approved Norco Bicycles Road medium medium 544.05 376.84 38647.0 Male 85 48.0 Health Affluent Customer No 6.0 QLD 3
4 1.0 Approved OHM Cycles Standard medium medium 183.86 137.90 34170.0 Female 5 35.0 Manufacturing High Net Worth Yes 12.0 NSW 3
5 1.0 Approved Solex Standard medium medium 1577.53 826.51 37220.0 Male 69 66.0 IT Mass Customer No 5.0 NSW 1
In [70]:
df.isnull().sum()
Out[70]:
online_order                           0
order_status                           0
brand                                  0
product_line                           0
product_class                          0
product_size                           0
list_price                             0
standard_cost                          0
product_first_sold_date                0
gender                                 0
past_3_years_bike_related_purchases    0
age                                    0
job_industry_category                  0
wealth_segment                         0
owns_car                               0
tenure                                 0
state                                  0
pred_kmeans                            0
dtype: int64
In [71]:
df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 13595 entries, 1 to 19961
Data columns (total 18 columns):
 #   Column                               Non-Null Count  Dtype  
---  ------                               --------------  -----  
 0   online_order                         13595 non-null  float64
 1   order_status                         13595 non-null  object 
 2   brand                                13595 non-null  object 
 3   product_line                         13595 non-null  object 
 4   product_class                        13595 non-null  object 
 5   product_size                         13595 non-null  object 
 6   list_price                           13595 non-null  float64
 7   standard_cost                        13595 non-null  float64
 8   product_first_sold_date              13595 non-null  float64
 9   gender                               13595 non-null  object 
 10  past_3_years_bike_related_purchases  13595 non-null  int64  
 11  age                                  13595 non-null  float64
 12  job_industry_category                13595 non-null  object 
 13  wealth_segment                       13595 non-null  object 
 14  owns_car                             13595 non-null  object 
 15  tenure                               13595 non-null  float64
 16  state                                13595 non-null  object 
 17  pred_kmeans                          13595 non-null  int32  
dtypes: float64(6), int32(1), int64(1), object(10)
memory usage: 2.5+ MB
In [72]:
personal = df[['gender','owns_car','state','wealth_segment','job_industry_category','brand','product_class','product_size','product_line','order_status']]
In [73]:
fig,ax = plt.subplots(2,3,figsize=(20,10))
sns.set_style('darkgrid')
for variable, subplot in zip(personal,ax.flatten()):
    sns.countplot(df[variable],hue=df['pred_kmeans'],ax=subplot,palette='cool')
In [74]:
other = df[['list_price','standard_cost','product_first_sold_date']]
In [75]:
fig,ax = plt.subplots(2,3,figsize=(20,10))
sns.set_style('darkgrid')
for variable, subplot in zip(other,ax.flatten()):
    sns.kdeplot(data= df,x=variable,hue='pred_kmeans',ax=subplot,palette='cool')
In [76]:
personalnum = df[['age','tenure']]
In [77]:
fig,ax = plt.subplots(2,3,figsize=(20,10))
sns.set_style('darkgrid')
for variable, subplot in zip(personalnum,ax.flatten()):
    sns.kdeplot(data= df,x=variable,hue='pred_kmeans',ax=subplot,palette='cool')
In [78]:
plt.figure(figsize=(20,10))
sns.scatterplot(data=df, x='age', y='list_price', hue='pred_kmeans' ,palette='cool');
plt.xlabel('Income', fontsize=20, labelpad=20)
plt.ylabel('Total Spendings', fontsize=20, labelpad=20);
In [79]:
 df.to_excel('OLD_data_forger.xls', index=False)
In [80]:
filepath2 = r"C:\Users\91623\OneDrive\Desktop\forage\newcous.xlsx"
fg= pd.read_excel(filepath2)
print(df)

       online_order order_status           brand product_line product_class  \
1               1.0     Approved        WeareA2B     Standard        medium   
2               0.0     Approved        WeareA2B     Standard        medium   
3               1.0     Approved  Norco Bicycles         Road        medium   
4               1.0     Approved      OHM Cycles     Standard        medium   
5               1.0     Approved           Solex     Standard        medium   
...             ...          ...             ...          ...           ...   
19957           0.0     Approved   Trek Bicycles     Standard        medium   
19958           0.0     Approved   Trek Bicycles     Standard        medium   
19959           0.0     Approved  Giant Bicycles     Standard          high   
19960           0.0     Approved  Norco Bicycles     Standard           low   
19961           0.0     Approved           Solex     Standard        medium   

      product_size  list_price  standard_cost  product_first_sold_date  \
1           medium     1769.64         108.76                  40672.0   
2           medium     1807.45         778.69                  40410.0   
3           medium      544.05         376.84                  38647.0   
4           medium      183.86         137.90                  34170.0   
5           medium     1577.53         826.51                  37220.0   
...            ...         ...            ...                      ...   
19957        small     1775.81        1580.47                  35160.0   
19958       medium      499.53         388.72                  36334.0   
19959       medium     1129.13         677.48                  40649.0   
19960       medium      958.74         748.90                  38693.0   
19961       medium     1483.20          99.59                  38647.0   

       gender  past_3_years_bike_related_purchases   age  \
1      Female                                   86  64.0   
2        Male                                   64  24.0   
3        Male                                   85  48.0   
4      Female                                    5  35.0   
5        Male                                   69  66.0   
...       ...                                  ...   ...   
19957  Female                                   75  26.0   
19958    Male                                   14  44.0   
19959    Male                                    9  48.0   
19960  Female                                   82  41.0   
19961    Male                                    2  45.0   

      job_industry_category     wealth_segment owns_car  tenure state  \
1                    Health     High Net Worth       No    12.0   NSW   
2                  Property      Mass Customer      Yes     5.0   VIC   
3                    Health  Affluent Customer       No     6.0   QLD   
4             Manufacturing     High Net Worth      Yes    12.0   NSW   
5                        IT      Mass Customer       No     5.0   NSW   
...                     ...                ...      ...     ...   ...   
19957                Retail      Mass Customer      Yes     5.0   NSW   
19958                Health  Affluent Customer       No    10.0   VIC   
19959                Retail  Affluent Customer      Yes    17.0   NSW   
19960    Financial Services     High Net Worth       No    14.0   QLD   
19961                Retail     High Net Worth      Yes    13.0   VIC   

       pred_kmeans  
1                1  
2                2  
3                3  
4                3  
5                1  
...            ...  
19957            4  
19958            3  
19959            4  
19960            4  
19961            1  

[13595 rows x 18 columns]
In [81]:
fg.head()
Out[81]:
first_name last_name gender past_3_years AGE DOB job_title job_industry_category wealth_segment owns_car tenure postcode state country Rank
0 Chickie Brister Male 86 64 1957-07-12 General Manager Manufacturing Mass Customer Yes 14 4500 QLD Australia 1
1 Morly Genery Male 69 52 1970-03-22 Structural Engineer Property Mass Customer No 16 2113 NSW Australia 1
2 Ardelis Forrester Female 10 47 1974-08-28 Senior Cost Accountant Financial Services Affluent Customer No 10 3505 VIC Australia 1
3 Lucine Stutt Female 64 43 1979-01-28 Account Representative III Manufacturing Affluent Customer Yes 5 4814 QLD Australia 4
4 Melinda Hadlee Female 34 56 1965-09-21 Financial Analyst Financial Services Affluent Customer No 19 2093 NSW Australia 4
In [82]:
fg=fg.drop('first_name',axis=1)
In [83]:
fg=fg.drop('last_name',axis=1)
In [84]:
fg=fg.drop('job_title',axis=1)
In [85]:
fg=fg.drop('Rank',axis=1)
In [86]:
unique = [feature for feature in fg.columns if len(fg[feature].unique())>0 and len(fg[feature].unique())<100]
for feature in unique:
    print("{} has {} unique values : {} {}".format(feature,len(fg[feature].unique()),fg[feature].unique(),"\n"))

gender has 3 unique values : ['Male' 'Female' 'U'] 

AGE has 65 unique values : [ 64  52  47  43  56  71  45  49  50  36  27  22  32  58  68  28  83  67
  53  39  76  69  55  61  42  70  35  66  37  75  65  72  44  23  40  41
 122  80  25  48  46  79  31  82  60  26  24  34  59  54  21  73  51  33
  29  57  30  78  63  77  38  81  74  62  20] 

job_industry_category has 9 unique values : ['Manufacturing' 'Property' 'Financial Services' 'Entertainment' 'Retail'
 'IT' 'Telecommunications' 'Health' 'Argiculture'] 

wealth_segment has 3 unique values : ['Mass Customer' 'Affluent Customer' 'High Net Worth'] 

owns_car has 2 unique values : ['Yes' 'No'] 

tenure has 23 unique values : [14 16 10  5 19 22  8 17  3  9  4 11 12 13  7 20 15  6 18 21  2  1  0] 

state has 3 unique values : ['QLD' 'NSW' 'VIC'] 

country has 1 unique values : ['Australia']
In [87]:
fg.head()
Out[87]:
gender past_3_years AGE DOB job_industry_category wealth_segment owns_car tenure postcode state country
0 Male 86 64 1957-07-12 Manufacturing Mass Customer Yes 14 4500 QLD Australia
1 Male 69 52 1970-03-22 Property Mass Customer No 16 2113 NSW Australia
2 Female 10 47 1974-08-28 Financial Services Affluent Customer No 10 3505 VIC Australia
3 Female 64 43 1979-01-28 Manufacturing Affluent Customer Yes 5 4814 QLD Australia
4 Female 34 56 1965-09-21 Financial Services Affluent Customer No 19 2093 NSW Australia
In [88]:
fg=fg.drop('country',axis=1)
In [89]:
fg.dropna()
Out[89]:
gender past_3_years AGE DOB job_industry_category wealth_segment owns_car tenure postcode state
0 Male 86 64 1957-07-12 Manufacturing Mass Customer Yes 14 4500 QLD
1 Male 69 52 1970-03-22 Property Mass Customer No 16 2113 NSW
2 Female 10 47 1974-08-28 Financial Services Affluent Customer No 10 3505 VIC
3 Female 64 43 1979-01-28 Manufacturing Affluent Customer Yes 5 4814 QLD
4 Female 34 56 1965-09-21 Financial Services Affluent Customer No 19 2093 NSW
... ... ... ... ... ... ... ... ... ... ...
830 Male 60 62 1959-10-07 Financial Services Affluent Customer No 9 2200 NSW
831 Male 22 20 2001-10-17 Health Mass Customer No 6 2196 NSW
832 Female 17 67 1954-10-05 Financial Services Affluent Customer Yes 15 4702 QLD
833 Male 30 69 1952-12-17 Financial Services Mass Customer Yes 19 4215 QLD
834 Male 56 66 1955-10-02 Property Mass Customer Yes 14 2010 NSW

818 rows × 10 columns

In [90]:
fg.describe()
Out[90]:
past_3_years AGE tenure postcode
count 835.000000 835.000000 835.000000 835.000000
mean 50.322156 51.758084 11.354491 3018.805988
std 27.560405 19.544855 4.996770 849.086917
min 0.000000 20.000000 0.000000 2000.000000
25% 28.000000 39.000000 7.000000 2210.500000
50% 51.000000 50.000000 11.000000 2871.000000
75% 72.000000 65.000000 15.000000 3818.000000
max 99.000000 122.000000 22.000000 4879.000000
In [91]:
fg.corr()
Out[91]:
past_3_years AGE tenure postcode
past_3_years 1.000000 -0.028114 -0.058121 0.014810
AGE -0.028114 1.000000 0.206565 0.012354
tenure -0.058121 0.206565 1.000000 0.036482
postcode 0.014810 0.012354 0.036482 1.000000
In [92]:
sns.countplot(fg['gender'],palette='dark:r');
In [93]:
sns.countplot(fg['state'],palette='dark:r');
In [94]:
sns.countplot(fg['wealth_segment'],palette='dark:r');
In [95]:
fg=fg.drop('postcode',axis=1)
In [96]:
fg=fg.drop('DOB',axis=1)
In [97]:
continuos_features = [feature for feature in numerical_features if feature not in discrete_features]
print("we have {} continuos features out of {} total features".format(len(continuos_features),len(fg.columns)))

we have 7 continuos features out of 8 total features
In [98]:
sns.pairplot(fg)
Out[98]:
<seaborn.axisgrid.PairGrid at 0x1902f017490>
In [99]:
corrmat=fg.corr()
top_corr=corrmat.index
plt.figure(figsize=(20,20))
f= sns.heatmap(fg[top_corr].corr(),annot= True,cmap="RdYlGn")
In [100]:
fg1=fg.copy()
In [101]:
fg.head()
Out[101]:
gender past_3_years AGE job_industry_category wealth_segment owns_car tenure state
0 Male 86 64 Manufacturing Mass Customer Yes 14 QLD
1 Male 69 52 Property Mass Customer No 16 NSW
2 Female 10 47 Financial Services Affluent Customer No 10 VIC
3 Female 64 43 Manufacturing Affluent Customer Yes 5 QLD
4 Female 34 56 Financial Services Affluent Customer No 19 NSW
In [102]:
fg1.head()
Out[102]:
gender past_3_years AGE job_industry_category wealth_segment owns_car tenure state
0 Male 86 64 Manufacturing Mass Customer Yes 14 QLD
1 Male 69 52 Property Mass Customer No 16 NSW
2 Female 10 47 Financial Services Affluent Customer No 10 VIC
3 Female 64 43 Manufacturing Affluent Customer Yes 5 QLD
4 Female 34 56 Financial Services Affluent Customer No 19 NSW
In [103]:
object_feat1 = [feature for feature in fg1.columns if fg1[feature].dtype == "O"]
fg1[object_feat1].head()
Out[103]:
gender job_industry_category wealth_segment owns_car state
0 Male Manufacturing Mass Customer Yes QLD
1 Male Property Mass Customer No NSW
2 Female Financial Services Affluent Customer No VIC
3 Female Manufacturing Affluent Customer Yes QLD
4 Female Financial Services Affluent Customer No NSW
In [104]:
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()

for i in object_feat1:
    fg1[i] = fg1[[i]].apply(le.fit_transform)
In [105]:
fg.head()
Out[105]:
gender past_3_years AGE job_industry_category wealth_segment owns_car tenure state
0 Male 86 64 Manufacturing Mass Customer Yes 14 QLD
1 Male 69 52 Property Mass Customer No 16 NSW
2 Female 10 47 Financial Services Affluent Customer No 10 VIC
3 Female 64 43 Manufacturing Affluent Customer Yes 5 QLD
4 Female 34 56 Financial Services Affluent Customer No 19 NSW
In [106]:
fg1.head()
Out[106]:
gender past_3_years AGE job_industry_category wealth_segment owns_car tenure state
0 1 86 64 5 2 1 14 1
1 1 69 52 6 2 0 16 0
2 0 10 47 2 0 0 10 2
3 0 64 43 5 0 1 5 1
4 0 34 56 2 0 0 19 0
In [107]:
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler() 
fg2 = scaler.fit_transform(fg1)
In [108]:
fg2 = pd.DataFrame(fg2,columns = fg1.columns)
In [109]:
from sklearn.cluster import KMeans
from yellowbrick.cluster import KElbowVisualizer
model = KMeans()
visualizer = KElbowVisualizer(model, k=(1,8))
visualizer.fit(fg2)
visualizer.show();
In [110]:
kmeans = KMeans(n_clusters=3,init='k-means++',random_state=0)
pred_kmeans = kmeans.fit_predict(fg1)
In [111]:
fg['pred_kmeans'] = pred_kmeans+1
fg.head()
Out[111]:
gender past_3_years AGE job_industry_category wealth_segment owns_car tenure state pred_kmeans
0 Male 86 64 Manufacturing Mass Customer Yes 14 QLD 1
1 Male 69 52 Property Mass Customer No 16 NSW 1
2 Female 10 47 Financial Services Affluent Customer No 10 VIC 2
3 Female 64 43 Manufacturing Affluent Customer Yes 5 QLD 1
4 Female 34 56 Financial Services Affluent Customer No 19 NSW 2
In [112]:
fg.isnull().sum()
Out[112]:
gender                   0
past_3_years             0
AGE                      0
job_industry_category    0
wealth_segment           0
owns_car                 0
tenure                   0
state                    0
pred_kmeans              0
dtype: int64
In [113]:
fg.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 835 entries, 0 to 834
Data columns (total 9 columns):
 #   Column                 Non-Null Count  Dtype 
---  ------                 --------------  ----- 
 0   gender                 835 non-null    object
 1   past_3_years           835 non-null    int64 
 2   AGE                    835 non-null    int64 
 3   job_industry_category  835 non-null    object
 4   wealth_segment         835 non-null    object
 5   owns_car               835 non-null    object
 6   tenure                 835 non-null    int64 
 7   state                  835 non-null    object
 8   pred_kmeans            835 non-null    int32 
dtypes: int32(1), int64(3), object(5)
memory usage: 55.6+ KB
In [114]:
personal = df[['gender','owns_car','state','wealth_segment','job_industry_category']]
In [115]:
fig,ax = plt.subplots(2,3,figsize=(20,10))
sns.set_style('darkgrid')
for variable, subplot in zip(personal,ax.flatten()):
    sns.countplot(fg[variable],hue=fg['pred_kmeans'],ax=subplot,palette='cool')
In [116]:
other = fg[['AGE','past_3_years','tenure']]
In [117]:
fig,ax = plt.subplots(2,3,figsize=(20,10))
sns.set_style('darkgrid')
for variable, subplot in zip(other,ax.flatten()):
    sns.kdeplot(data= fg,x=variable,hue='pred_kmeans',ax=subplot,palette='cool')
In [118]:
 fg.to_excel('new_data_forger.xls', index=False)
In [1]:
pip install -U notebook-as-pdf

Collecting notebook-as-pdf
  Downloading notebook_as_pdf-0.5.0-py3-none-any.whl (6.5 kB)
Requirement already satisfied, skipping upgrade: pyppeteer in c:\users\91623\anaconda3\lib\site-packages (from notebook-as-pdf) (0.2.2)
Collecting PyPDF2
  Downloading PyPDF2-1.28.4-py3-none-any.whl (87 kB)
Requirement already satisfied, skipping upgrade: nbconvert in c:\users\91623\anaconda3\lib\site-packages (from notebook-as-pdf) (6.0.7)
Requirement already satisfied, skipping upgrade: tqdm<5.0.0,>=4.42.1 in c:\users\91623\anaconda3\lib\site-packages (from pyppeteer->notebook-as-pdf) (4.50.2)
Requirement already satisfied, skipping upgrade: urllib3<2.0.0,>=1.25.8 in c:\users\91623\anaconda3\lib\site-packages (from pyppeteer->notebook-as-pdf) (1.25.11)
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Installing collected packages: PyPDF2, notebook-as-pdf
Successfully installed PyPDF2-1.28.4 notebook-as-pdf-0.5.0
In [8]:
pip install nbconvert

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Note: you may need to restart the kernel to use updated packages.
In [ ]: